CN114594532A - Method and device for predicting cold weather, electronic equipment and computer readable medium - Google Patents

Method and device for predicting cold weather, electronic equipment and computer readable medium Download PDF

Info

Publication number
CN114594532A
CN114594532A CN202210222268.4A CN202210222268A CN114594532A CN 114594532 A CN114594532 A CN 114594532A CN 202210222268 A CN202210222268 A CN 202210222268A CN 114594532 A CN114594532 A CN 114594532A
Authority
CN
China
Prior art keywords
target area
cold
characteristic curve
temperature
weather
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202210222268.4A
Other languages
Chinese (zh)
Other versions
CN114594532B (en
Inventor
张志远
黄耀海
于波
赵玮
郭金兰
张迎新
季崇萍
董彬
申鸿怡
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Meteorological Observatory
Beijing Moji Fengyun Technology Co ltd
Peking University
Original Assignee
Beijing Meteorological Observatory
Beijing Moji Fengyun Technology Co ltd
Peking University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Meteorological Observatory, Beijing Moji Fengyun Technology Co ltd, Peking University filed Critical Beijing Meteorological Observatory
Priority to CN202210222268.4A priority Critical patent/CN114594532B/en
Publication of CN114594532A publication Critical patent/CN114594532A/en
Application granted granted Critical
Publication of CN114594532B publication Critical patent/CN114594532B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/10—Devices for predicting weather conditions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Mathematical Physics (AREA)
  • Computing Systems (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Health & Medical Sciences (AREA)
  • Environmental & Geological Engineering (AREA)
  • Atmospheric Sciences (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Ecology (AREA)
  • Environmental Sciences (AREA)
  • Medical Informatics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The application relates to a method and a device for predicting cold weather, electronic equipment and a computer readable medium. The method comprises the following steps: acquiring temperature data of a plurality of observation stations in a target area at a plurality of preset time points; generating average temperatures of the target area at a plurality of preset times based on the temperature data of the plurality of observation stations at the plurality of preset time points; generating a characteristic curve based on the average temperature of the target area at the plurality of preset times; and identifying the cold weather of the target area based on the characteristic curve. According to the cold weather forecasting method, the cold weather forecasting device, the electronic equipment and the computer readable medium, the influence of artificial subjective analysis can be eliminated, the change characteristic of the temperature in the time dimension is extracted, the cold weather is automatically identified according to the change characteristic, and the identification speed and the identification accuracy are improved.

Description

Method and device for predicting cold weather, electronic equipment and computer readable medium
Technical Field
The present application relates to the field of weather prediction, and in particular, to a method and an apparatus for predicting cold weather, an electronic device, and a computer-readable medium.
Background
Cold tide is a natural weather phenomenon. The cold air from high latitude areas is rapidly strengthened under specific weather conditions and invades into low and medium latitude areas, so that large-range severe cooling, strong wind and rain and snow weather of the areas along the way are caused. The south invasion of cold air to a certain standard is called cold tide. The method can be used in autumn, winter and spring, and has great influence on the large-scale national soil of China, is accompanied by disastrous weather phenomena such as strong wind cooling, sudden snow, freezing, sand storm and the like, causes secondary disasters such as low temperature, frost, accumulated snow freezing, road and electric wire icing and the like, has great and profound influence on the life and property of people and even social stability, and can directly serve the working and living aspects of hundreds of millions of people when the cold tide process is accurately forecasted each time.
The current flow of artificial cold tide prediction is as follows: specifically, in the 500hPa circulation situation analysis process, a forecaster firstly marks potential height values of each grid point on a map one by one, then performs contour analysis on a potential height field, and respectively judges weather system properties, positions, intensities, movements and other information formed by cold and warm air according to the conditions of trends, bends, turns, closings and the like of different contours, and comprehensively separates circulation situations on the basis, each link of the process shows higher technical requirements, and objective automatic identification has greater difficulty.
At present, the cold tide is basically identified through the manual operation of forecasters, and the meteorological field data such as potential height, air temperature, wind and the like are analyzed according to the experience of the forecasters. Since this operation requires a lot of manual interpretation work, there are great individual differences in the forecast of cold tides. There are also techniques for quantifying and automatically identifying the above operations. But simply the handling of meteorological elements. The current technology has low accuracy rate for forecasting cold weather and long time consumption.
Therefore, a new method, apparatus, electronic device and computer readable medium for predicting cold weather is needed.
The above information disclosed in this background section is only for enhancement of understanding of the background of the application and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of this, the present application provides a method, an apparatus, an electronic device and a computer readable medium for predicting cold weather, which can eliminate the influence of artificial subjective analysis, extract the variation characteristics of temperature in the time dimension, and thus automatically identify cold weather, thereby improving the identification speed and accuracy.
Other features and advantages of the present application will be apparent from the following detailed description, or may be learned by practice of the application.
According to an aspect of the present application, a method for predicting cold weather is provided, the method including: acquiring temperature data of a plurality of observation stations in a target area at a plurality of preset time points; generating average temperatures of the target area at a plurality of preset times based on the temperature data of the plurality of observation stations at the plurality of preset time points; generating a characteristic curve based on the average temperature of the target area at the plurality of preset times; and identifying the cold weather of the target area based on the characteristic curve.
In an exemplary embodiment of the present application, acquiring temperature data of a plurality of observation stations in a target area at a plurality of preset time points includes: obtaining 2 m temperature data of EC of a plurality of observation stations in a target area at a plurality of preset time points.
In an exemplary embodiment of the present application, generating a characteristic curve based on the average temperature of the target region at the plurality of preset times includes: taking the average temperature as a vertical axis and a plurality of preset times as a horizontal axis to generate a time-temperature characteristic curve; or the gradient change of the average temperature is taken as a vertical axis, and a plurality of preset times are taken as a horizontal axis to generate a time gradient characteristic curve.
In an exemplary embodiment of the present application, when the characteristic curve is a time-temperature characteristic curve, identifying cold weather of the target area based on the characteristic curve includes: performing similarity matching on the time temperature characteristic curve and a plurality of historical time temperature characteristic curves; and identifying the cold weather of the target area according to the similarity matching result.
In an exemplary embodiment of the present application, when the characteristic curve is a time-temperature characteristic curve, identifying cold weather of the target area based on the characteristic curve includes: inputting the time temperature characteristic curve into a cold tide identification model to generate a cold tide probability; and when the cold tide probability is larger than a cold tide threshold value, determining the cold tide weather in the target area.
In an exemplary embodiment of the present application, when the characteristic curve is a time gradient characteristic curve, identifying cold weather of the target area based on the characteristic curve includes: generating a multi-dimensional vector according to the time gradient characteristic curve; generating element characteristics according to EC forecast elements of the current target area; inputting the multi-dimensional vector and the element characteristics into a cold tide identification model to generate a cold tide probability; and when the cold tide probability is larger than a cold tide threshold value, determining the cold tide weather in the target area.
In an exemplary embodiment of the present application, generating element features from EC forecast elements of the current target area includes: inputting the EC forecast elements of the current target area into a feature extraction model to generate element features.
In an exemplary embodiment of the present application, inputting the multidimensional vector and the feature into a cold tide identification model includes: performing data fusion on the multi-dimensional vectors and the element features, wherein the data fusion comprises splicing or averaging; and inputting the fused data into a cold tide identification model.
In an exemplary embodiment of the present application, further comprising: acquiring characteristic curves of a plurality of historical cold weather and normal weather of the target area; training a first machine learning model through a plurality of historical cold weather and characteristic curves of a plurality of historical normal weather, wherein the first machine learning model is a classification network model based on resnet; and when the calculation result of the first machine learning model is converged, generating the cold tide identification model.
In an exemplary embodiment of the present application, further comprising: acquiring a plurality of EC forecast elements of the target area; training a second machine learning model through a plurality of EC forecasting elements, wherein the second machine learning model is a transformer-based neural network model; and when the calculation result of the second machine learning model converges, generating the feature extraction model.
According to an aspect of the present application, there is provided a cold weather prediction apparatus, including: the temperature module is used for acquiring temperature data of a plurality of observation stations in a target area at a plurality of preset time points; the average module is used for generating average temperatures of the target area at a plurality of preset times based on the temperature data of the plurality of observation stations at a plurality of preset time points; a curve module for generating a characteristic curve based on the average temperature of the target region at the plurality of preset times; and the identification module is used for identifying the cold weather of the target area based on the characteristic curve.
According to an aspect of the present application, an electronic device is provided, the electronic device including: one or more processors; storage means for storing one or more programs; when executed by one or more processors, cause the one or more processors to implement a method as above.
According to an aspect of the application, a computer-readable medium is proposed, on which a computer program is stored, which program, when being executed by a processor, carries out the method as above.
According to the method, the device, the electronic equipment and the computer readable medium for predicting the cold weather, the temperature data of a plurality of observation stations in a target area at a plurality of preset time points are obtained; generating average temperatures of the target area at a plurality of preset times based on the temperature data of the plurality of observation stations at the plurality of preset time points; generating a characteristic curve based on the average temperature of the target area at the plurality of preset times; the method for identifying the cold weather of the target area based on the characteristic curve can eliminate the influence of artificial subjective analysis, extract the change characteristic of the temperature in the time dimension, automatically identify the cold weather and improve the identification speed and accuracy.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The above and other objects, features and advantages of the present application will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are only some embodiments of the present application and other drawings may be derived by those skilled in the art without inventive effort.
Fig. 1 is a system block diagram illustrating a method and apparatus for predicting cold weather according to an exemplary embodiment.
FIG. 2 is a flow chart illustrating a method of predicting cold weather according to an exemplary embodiment.
Fig. 3 is a flow chart illustrating a method of predicting cold weather according to another exemplary embodiment.
Fig. 4 is a flowchart illustrating a method of predicting cold weather according to another exemplary embodiment.
Fig. 5 is a block diagram illustrating a cold weather prediction apparatus according to an example embodiment.
FIG. 6 is a block diagram illustrating an electronic device in accordance with an example embodiment.
FIG. 7 is a block diagram illustrating a computer-readable medium in accordance with an example embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the subject matter of the present application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the application.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another. Thus, a first component discussed below may be termed a second component without departing from the teachings of the present concepts. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be appreciated by those skilled in the art that the drawings are merely schematic representations of exemplary embodiments, and that the blocks or processes shown in the drawings are not necessarily required to practice the present application and are, therefore, not intended to limit the scope of the present application.
The inventor of the present application thinks that the most fundamental factor capable of reflecting the cold tide is the temperature, so the present application starts from the temperature, extracts the change characteristics of the temperature in the time dimension, and uses the change characteristics to judge the occurrence of the cold tide. The influence of artificial subjective analysis is eliminated, and the extracted temperature change characteristic is relatively stable and can reflect the cold tide, so that the method can be used for automatically detecting the occurrence of the cold tide.
According to the cold tide weather prediction method, the statistical characteristics are obtained by counting the EC2 meter temperature change curve when the cold tide occurs, and the arrival of the cold tide is judged according to the statistical characteristics. The content of the present application is described in detail below with the aid of specific examples.
Fig. 1 is a system block diagram illustrating a method and apparatus for predicting cold weather according to an exemplary embodiment.
As shown in FIG. 1, the system architecture 10 may include observation sites 101, 102, 103, a network 104, and a server 105. Network 104 is used to provide a medium for communication links between observation sites 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The observation stations 101, 102, 103 interact with a server 105 over a network 104 to receive or send messages or the like. The observation stations 101, 102, 103 may have various monitoring applications installed thereon, such as a temperature monitoring application, a humidity monitoring application, a wind speed monitoring application, an air quality monitoring application, and so on.
The observation stations 101, 102, 103 may be various electronic devices having a monitoring function and supporting network data transmission.
Server 105 may be a server that provides various services, such as a back-office management server that provides analysis of temperature data acquired by observation sites 101, 102, 103. The background management server may analyze the received temperature data, and feed back a processing result (e.g., whether cold weather exists) to the administrator.
The server 105 may, for example, obtain temperature data of a plurality of observation sites within the target area at a plurality of preset time points; the server 105 may generate average temperatures of the target area at a plurality of preset times, for example, based on the temperature data of the plurality of observation sites at the plurality of preset time points; server 105 may generate a characteristic curve, for example, based on the average temperature of the target area at the plurality of preset times; the server 105 may identify cold weather for the target area, for example, based on the characteristic curve.
The server 105 may also, for example, obtain a plurality of historical cold weather and a plurality of historical normal weather profiles for the target area; training a first machine learning model through a plurality of historical cold weather and characteristic curves of a plurality of historical normal weather, wherein the first machine learning model is a classification network model based on resnet; and when the calculation result of the first machine learning model is converged, generating the cold tide identification model.
The server 105 may also, for example, obtain a plurality of EC forecast elements for the target area; training a second machine learning model through a plurality of EC forecasting elements, wherein the second machine learning model is a transformer-based neural network model; and when the calculation result of the second machine learning model converges, generating the feature extraction model.
The server 105 may be an entity server, or may be composed of a plurality of servers, for example, it should be noted that the method for predicting cold weather provided by the embodiment of the present application may be executed by the server 105, and accordingly, the device for predicting cold weather may be disposed in the server 105. While the application end providing temperature monitoring is typically located in the observation stations 101, 102, 103.
The characteristic can be well used for describing the cold tide, the distinguishing between the cold tide and normal weather is obvious, the characteristic curve can be directly used for judging the cold tide, and meanwhile, the curve made by the characteristic can also be used for carrying out automatic classification on the cold tide by a cold tide identification model. Furthermore, the characteristics in the cold tide weather prediction method can be fused with other EC elements and characteristics to automatically classify the cold tide. And the stability and the accuracy of cold tide classification are greatly improved by comparison and judgment.
FIG. 2 is a flow chart illustrating a method of predicting cold weather according to an exemplary embodiment. The method 20 for predicting cold weather includes at least steps S202 to S208.
As shown in fig. 2, in S202, temperature data of a plurality of observation stations in a target area at a plurality of preset time points is acquired. The 2-meter temperature data of the ECC at a plurality of preset time points for a plurality of observation stations within the target area may be obtained.
In S204, average temperatures of the target area at a plurality of preset times are generated based on the temperature data of the plurality of observation stations at the plurality of preset time points.
In S206, a characteristic curve is generated based on the average temperature of the target region at the plurality of preset times. And taking the average temperature as a vertical axis and a plurality of preset times as a horizontal axis to generate a time-temperature characteristic curve.
In S208, the cold weather of the target area is identified based on the characteristic curve. Performing similarity matching on the time temperature characteristic curve and a plurality of historical time temperature characteristic curves; and identifying the cold weather of the target area according to the similarity matching result.
In a particular embodiment, 2 meters of temperature data may be acquired for the current day's EC extension. The average temperature of the site locations is then calculated for hours within the designated area, assuming XiAnd i is the average of all temperatures in a given area for 24 hours of the day {0,1,2, …,23 }. Then Xi＝average(Tij) And j is {0,1,2, …, N }, where N is a position number for specifying an observation station within the area. Mixing XiDrawing a curve based on time change, and performing regular identification on the curve by using a curve comparison method to further judge whether the weather has cold tide or not
According to the method for predicting the cold weather, the temperature data of a plurality of observation stations in a target area at a plurality of preset time points are obtained; generating average temperatures of the target area at a plurality of preset times based on the temperature data of the plurality of observation stations at the plurality of preset time points; generating a characteristic curve based on the average temperature of the target area at the plurality of preset times; the method for identifying the cold tide weather of the target area based on the characteristic curve can eliminate the influence of artificial subjective analysis, extract the change characteristic of the temperature in the time dimension, automatically identify the cold tide weather and improve the identification speed and accuracy.
It should be clearly understood that this application describes how to make and use particular examples, but the principles of this application are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
Fig. 3 is a flow chart illustrating a method of predicting cold weather according to another exemplary embodiment. The process 30 shown in fig. 3 is a supplementary description of the process shown in fig. 2.
As shown in fig. 3, in S302, temperature data of a plurality of observation stations in a target area at a plurality of preset time points is acquired.
In S304, an average temperature of the target area at a plurality of preset times is generated based on the temperature data of the plurality of observation stations at the plurality of preset time points.
In S306, a characteristic curve is generated based on the average temperature of the target region at the plurality of preset times. And taking the average temperature as a vertical axis and a plurality of preset times as a horizontal axis to generate a time-temperature characteristic curve.
In S308, the time-temperature characteristic curve is input to a cold tide identification model for cold tide weather identification. More specifically, the time temperature characteristic curve can be input into a cold tide identification model to generate a cold tide probability; and when the cold tide probability is larger than a cold tide threshold value, determining the cold tide weather in the target area.
In a particular embodiment, 2 meters of temperature data may be acquired for the current day's EC extension. The average temperature of the site locations is then calculated for hours within the designated area, assuming XiAnd i is the average of all temperatures in a given area for 24 hours of the day {0,1,2, …,23 }. Then Xi＝average(Tij) And j is {0,1,2, …, N }, where N is a position number for specifying an observation station within the area. Mixing XiAnd drawing a curve based on time change, and then introducing the temperature change curve serving as a characteristic graph into a model for cold tide classification to finally obtain a cold tide judgment result.
More specifically, the cold tide identification model may be a convolutional neural network based model, such as a resnet based classification network. According to the application, a cold tide recognition model can be constructed through a RestNet3D model, and more specifically, according to the size of training data, an 18-layer model or a 34-layer model can be taken:
Figure BDA0003537937860000091
in one embodiment, further comprising: acquiring characteristic curves of a plurality of historical cold weather and normal weather of the target area; training a first machine learning model through a plurality of historical cold weather and characteristic curves of a plurality of historical normal weather, wherein the first machine learning model is a classification network model based on resnet; and when the calculation result of the first machine learning model is converged, generating the cold tide identification model.
Fig. 4 is a flowchart illustrating a method of predicting cold weather according to another exemplary embodiment. The flow 40 shown in fig. 4 is a supplementary description of the flow shown in fig. 2.
As shown in fig. 4, in S402, temperature data of a plurality of observation stations in a target area at a plurality of preset time points is acquired.
In S404, average temperatures of the target area at a plurality of preset times are generated based on the temperature data of the plurality of observation stations at the plurality of preset time points.
In S406, a time gradient characteristic curve is generated with the gradient change of the average temperature as the vertical axis and the plurality of preset times as the horizontal axis.
In S408, a multidimensional vector is generated from the time gradient profile.
In S410, the EC forecast element of the current target area is input into a feature extraction model to generate element features.
In one embodiment, further comprising: acquiring a plurality of EC forecast elements of the target area; training a second machine learning model through a plurality of EC forecasting elements, wherein the second machine learning model is a transformer-based neural network model; and when the calculation result of the second machine learning model converges, generating the feature extraction model.
The EC element value feature extraction model can be a convolutional neural network or a transformer-based neural network. Furthermore, the classifier for final feature fusion can be implemented by using MLP or a conventional machine learning classifier, such as SVM. EC forecast elements may include: temperature, humidity, wind speed, wind direction, pressure, etc.
In S412, the multi-dimensional vector and the feature are input into a cold tide identification model to identify cold tide weather. Performing data fusion on the multi-dimensional vectors and the element features, wherein the data fusion comprises splicing or averaging; and inputting the fused data into a cold tide identification model.
In a particular embodiment, 2 meters of temperature data may be acquired for the current day's EC extension. The average temperature of the site locations is then calculated for hours within the designated area, assuming XiAnd i is the average of all temperatures in a given area for 24 hours of the day {0,1,2, …,23 }. Then Xi＝average(Tij) And j is {0,1,2, …, N }, where N is a position number for specifying an observation station within the area.
Let Yi＝Xi+1-XiWhere i ═ {0,1,2, …,23} represents a temperature change for a temperature gradient, a characteristic about temperature may be composed; taking the numerical value of the EC forecast element on the same day or the numerical value of the EC forecast element, and extracting the characteristic value of the network through a model; and performing data fusion on the multi-dimensional vector and the element characteristics, wherein the data fusion can be splicing or summation. And inputting the fused features into a classification model for final classification of cold tides.
In one embodiment, the multi-dimensional vector and the feature can be input into a cold tide identification model to generate a cold tide probability; and when the cold tide probability is larger than a cold tide threshold value, determining the cold tide weather in the target area.
Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as computer programs executed by a CPU. When executed by the CPU, performs the functions defined by the methods provided herein. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.
Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the method according to exemplary embodiments of the present application, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
The following are embodiments of the apparatus of the present application that may be used to perform embodiments of the method of the present application. For details which are not disclosed in the embodiments of the apparatus of the present application, reference is made to the embodiments of the method of the present application.
Fig. 5 is a block diagram illustrating a cold weather prediction apparatus according to an example embodiment. As shown in fig. 5, the cold weather prediction apparatus 50 includes: a temperature module 502, an averaging module 504, a curve module 506, and an identification module 508.
The temperature module 502 is configured to obtain temperature data of multiple observation stations in a target area at multiple preset time points; a curve module 506 and an identification module 508.
The averaging module 504 is configured to generate average temperatures of the target area at a plurality of preset times based on the temperature data of the plurality of observation stations at the plurality of preset time points;
the curve module 506 is configured to generate a characteristic curve based on the average temperature of the target region at the plurality of preset times;
the identification module 508 is configured to identify cold weather of the target area based on the characteristic curve.
According to the cold weather prediction device, the temperature data of a plurality of observation stations in a target area at a plurality of preset time points are obtained; generating average temperatures of the target area at a plurality of preset times based on the temperature data of the plurality of observation stations at the plurality of preset time points; generating a characteristic curve based on the average temperature of the target area at the plurality of preset times; the method for identifying the cold weather of the target area based on the characteristic curve can eliminate the influence of artificial subjective analysis, extract the change characteristic of the temperature in the time dimension, automatically identify the cold weather and improve the identification speed and accuracy.
FIG. 6 is a block diagram illustrating an electronic device in accordance with an example embodiment.
An electronic device 600 according to this embodiment of the present application is described below with reference to fig. 6. The electronic device 600 shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 6, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 that connects the various system components (including the storage unit 620 and the processing unit 610), a display unit 640, and the like.
Wherein the storage unit stores program code executable by the processing unit 610 to cause the processing unit 610 to perform steps according to various exemplary embodiments of the present application described in the present specification. For example, the processing unit 610 may perform the steps as shown in fig. 2, 3, 4.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
Bus 630 may be one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 600 can also communicate with one or more external devices 600' (e.g., keyboard, pointing device, bluetooth device, etc.) such that a user can communicate with the devices with which the electronic device 600 interacts, and/or any device (e.g., router, modem, etc.) with which the electronic device 600 can communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, and may also be implemented by software in combination with necessary hardware. Therefore, as shown in fig. 7, the technical solution according to the embodiment of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiment of the present application.
The software product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The computer readable medium carries one or more programs which, when executed by a device, cause the computer readable medium to perform the functions of: acquiring temperature data of a plurality of observation stations in a target area at a plurality of preset time points; generating average temperatures of the target area at a plurality of preset times based on the temperature data of the plurality of observation stations at the plurality of preset time points; generating a characteristic curve based on the average temperature of the target area at the plurality of preset times; and identifying the cold weather of the target area based on the characteristic curve.
Those skilled in the art will appreciate that the modules described above may be distributed in the apparatus according to the description of the embodiments, or may be modified accordingly in one or more apparatuses unique from the embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiment of the present application.
Exemplary embodiments of the present application are specifically illustrated and described above. It is to be understood that the application is not limited to the details of construction, arrangement, or method of implementation described herein; on the contrary, the intention is to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims (13)

1. A cold weather prediction method is characterized by comprising the following steps:
acquiring temperature data of a plurality of observation stations in a target area at a plurality of preset time points;
generating average temperatures of the target area at a plurality of preset times based on temperature data of the plurality of observation stations at a plurality of preset time points;
generating a characteristic curve based on the average temperature of the target area at the plurality of preset times;
and identifying the cold weather of the target area based on the characteristic curve.
2. The method of claim 1, wherein obtaining temperature data for a plurality of observation sites within a target area at a plurality of predetermined time points comprises:
and obtaining 2 m temperature data in the EC data of a plurality of observation stations in the target area at a plurality of preset time points.
3. The method of claim 1, wherein generating a profile based on the average temperature of the target region at the plurality of preset times comprises:
taking the average temperature as a vertical axis and a plurality of preset times as a horizontal axis to generate a time-temperature characteristic curve; or
And taking the gradient change of the average temperature as a vertical axis and a plurality of preset times as a horizontal axis to generate a time gradient characteristic curve.
4. The method of claim 1, wherein identifying cold weather in the target region based on the characteristic curve when the characteristic curve is a time-temperature characteristic curve comprises:
performing similarity matching on the time temperature characteristic curve and a plurality of historical time temperature characteristic curves;
and identifying the cold tide weather of the target area according to the similarity matching result.
5. The method of claim 1, wherein identifying cold weather in the target region based on the characteristic curve when the characteristic curve is a time-temperature characteristic curve comprises:
inputting the time temperature characteristic curve into a cold tide identification model to generate a cold tide probability;
and when the cold tide probability is larger than a cold tide threshold value, determining the cold tide weather in the target area.
6. The method of claim 1, wherein identifying cold weather in the target region based on the characteristic curve when the characteristic curve is a time gradient characteristic curve comprises:
generating a multi-dimensional vector according to the time gradient characteristic curve;
generating element characteristics according to EC forecast elements of the current target area;
inputting the multi-dimensional vectors and the element characteristics into a cold tide identification model to generate a cold tide probability;
and when the cold tide probability is larger than a cold tide threshold value, determining the cold tide weather in the target area.
7. The method of claim 6, wherein generating element features from EC forecast elements for the current target area comprises:
inputting the EC forecast elements of the current target area into a feature extraction model to generate element features.
8. The method of claim 6, wherein inputting the multi-dimensional vectors and the feature features into a cold tide recognition model comprises:
performing data fusion on the multi-dimensional vectors and the element features, wherein the data fusion comprises splicing or averaging;
and inputting the fused data into a cold tide identification model.
9. The method of claim 5 or 6, further comprising:
acquiring characteristic curves of a plurality of historical cold weather and normal weather of the target area;
training a first machine learning model through a plurality of historical cold weather and characteristic curves of a plurality of historical normal weather, wherein the first machine learning model is a classification network model based on resnet;
and when the calculation result of the first machine learning model is converged, generating the cold tide identification model.
10. The method of claim 7, further comprising:
acquiring a plurality of EC forecast elements of the target area;
training a second machine learning model through a plurality of EC forecasting elements, wherein the second machine learning model is a transformer-based neural network model;
and when the calculation result of the second machine learning model converges, generating the feature extraction model.
11. A cold weather prediction device, comprising:
the temperature module is used for acquiring temperature data of a plurality of observation stations in a target area at a plurality of preset time points;
the average module is used for generating average temperatures of the target area at a plurality of preset times based on the temperature data of the plurality of observation stations at a plurality of preset time points;
a curve module for generating a characteristic curve based on the average temperature of the target region at the plurality of preset times;
and the identification module is used for identifying the cold weather of the target area based on the characteristic curve.
12. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-10.
13. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-10.
CN202210222268.4A 2022-03-09 2022-03-09 Cold weather prediction method and device, electronic equipment and computer readable medium Active CN114594532B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210222268.4A CN114594532B (en) 2022-03-09 2022-03-09 Cold weather prediction method and device, electronic equipment and computer readable medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210222268.4A CN114594532B (en) 2022-03-09 2022-03-09 Cold weather prediction method and device, electronic equipment and computer readable medium

Publications (2)

Publication Number Publication Date
CN114594532A true CN114594532A (en) 2022-06-07
CN114594532B CN114594532B (en) 2024-06-14

Family

ID=81808434

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210222268.4A Active CN114594532B (en) 2022-03-09 2022-03-09 Cold weather prediction method and device, electronic equipment and computer readable medium

Country Status (1)

Country Link
CN (1) CN114594532B (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115640467A (en) * 2022-10-19 2023-01-24 中科三清科技有限公司 Method for determining weather state, and method and device for constructing feature database
CN117631090A (en) * 2024-01-25 2024-03-01 南京信息工程大学 Cold tide identification method and device
CN118297212A (en) * 2024-03-15 2024-07-05 国家气候中心 Low-temperature anti-freezing risk prediction system and method

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011127313A1 (en) * 2010-04-08 2011-10-13 Energyresource Management Corp Energy-saving measurement, adjustment and monetization system and method
JP2016142555A (en) * 2015-01-30 2016-08-08 中国電力株式会社 Atmospheric temperature prediction system, atmospheric temperature prediction method, and program
KR20160144814A (en) * 2015-06-09 2016-12-19 대한민국(기상청장) System and method for providing a weather information based on grid
CN107577916A (en) * 2017-10-11 2018-01-12 中国科学院寒区旱区环境与工程研究所 Frozen soil index based on region determines method and electronic equipment
CN111178635A (en) * 2019-12-31 2020-05-19 上海眼控科技股份有限公司 Weather forecast method, weather forecast device, computer equipment and computer readable storage medium
KR20200068055A (en) * 2018-11-26 2020-06-15 디토닉 주식회사 System and Method for Predicting Road Surface State
CN111414991A (en) * 2020-02-21 2020-07-14 中国人民解放军国防科技大学 Meteorological frontal surface automatic identification method based on multivariate regression
CN112285807A (en) * 2019-11-12 2021-01-29 北京嘉韵楷达气象科技有限公司 Meteorological information prediction method and device
CN112308281A (en) * 2019-11-12 2021-02-02 北京嘉韵楷达气象科技有限公司 Temperature information prediction method and device
US20210215848A1 (en) * 2020-01-14 2021-07-15 ZineOne, Inc. Network weather intelligence system

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011127313A1 (en) * 2010-04-08 2011-10-13 Energyresource Management Corp Energy-saving measurement, adjustment and monetization system and method
JP2016142555A (en) * 2015-01-30 2016-08-08 中国電力株式会社 Atmospheric temperature prediction system, atmospheric temperature prediction method, and program
KR20160144814A (en) * 2015-06-09 2016-12-19 대한민국(기상청장) System and method for providing a weather information based on grid
CN107577916A (en) * 2017-10-11 2018-01-12 中国科学院寒区旱区环境与工程研究所 Frozen soil index based on region determines method and electronic equipment
KR20200068055A (en) * 2018-11-26 2020-06-15 디토닉 주식회사 System and Method for Predicting Road Surface State
CN112285807A (en) * 2019-11-12 2021-01-29 北京嘉韵楷达气象科技有限公司 Meteorological information prediction method and device
CN112308281A (en) * 2019-11-12 2021-02-02 北京嘉韵楷达气象科技有限公司 Temperature information prediction method and device
CN111178635A (en) * 2019-12-31 2020-05-19 上海眼控科技股份有限公司 Weather forecast method, weather forecast device, computer equipment and computer readable storage medium
US20210215848A1 (en) * 2020-01-14 2021-07-15 ZineOne, Inc. Network weather intelligence system
CN111414991A (en) * 2020-02-21 2020-07-14 中国人民解放军国防科技大学 Meteorological frontal surface automatic identification method based on multivariate regression

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115640467A (en) * 2022-10-19 2023-01-24 中科三清科技有限公司 Method for determining weather state, and method and device for constructing feature database
CN117631090A (en) * 2024-01-25 2024-03-01 南京信息工程大学 Cold tide identification method and device
CN117631090B (en) * 2024-01-25 2024-05-14 南京信息工程大学 Cold tide identification method and device
CN118297212A (en) * 2024-03-15 2024-07-05 国家气候中心 Low-temperature anti-freezing risk prediction system and method

Also Published As

Publication number Publication date
CN114594532B (en) 2024-06-14

Similar Documents

Publication Publication Date Title
CN114594532B (en) Cold weather prediction method and device, electronic equipment and computer readable medium
CN112285807B (en) Meteorological information prediction method and device
CN107728234B (en) Thunder and lightning strength value prediction method based on atmospheric electric field data
US11689154B2 (en) Systems and methods for distributed-solar power forecasting using parameter regularization
US20180306762A1 (en) Automatic siting for air quality monitoring stations
Hong et al. Basis for recommending an update of wind velocity pressures in Canadian design codes
CN110189026B (en) Method and device for evaluating air quality improvement measure, medium, and electronic device
US8670782B2 (en) Systems and methods for analyzing spatiotemporally ambiguous events
CN107463950B (en) Contact network fault prediction method based on external meteorological environment influence
CN110738355A (en) urban waterlogging prediction method based on neural network
CN112149887A (en) PM2.5 concentration prediction method based on data space-time characteristics
CN117518299B (en) Classified strong convection proximity probability forecasting method, system, equipment and terminal
JP5931830B2 (en) Blackout number prediction device, blackout number prediction method and program
CN115019209A (en) Method and system for detecting state of electric power tower based on deep learning
Rezaei et al. A probabilistic framework based on statistical learning theory for structural reliability analysis of transmission line systems
Hou et al. Spatial distribution assessment of power outage under typhoon disasters
CN112541636A (en) Power transmission line icing risk early warning method and device, medium and electronic equipment
Doostan et al. Predicting lightning-related outages in power distribution systems: A statistical approach
CN117172554A (en) Icing disaster risk prediction method, device, equipment and storage medium
Lu et al. An integrated damage modeling and assessment framework for overhead power distribution systems considering tree-failure risks
Sharma et al. Forecasting weather‐related power outages using weighted logistic regression
Hou et al. Modeling the resilience of power distribution systems subjected to extreme winds considering tree failures: An integrated framework
JP2003090887A (en) Predication system and prediction method of instantaneous voltage drop by thunderbolt
CN112683916A (en) Method and device for identifying missing or mounting error of small hardware fittings of current collecting line tower
CN113408816B (en) Power grid disaster situation assessment method based on deep neural network

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant